The dataset includes spectral correlation function (SCF) estimations by FFT accumulation method (FAM) for totally 4500 signals with 20000 I/Q samples (but only 16384 samples are used). The signals belong to three different cellular communication standards: GSM, WCDMA, and LTE. The signals have been received from the different channels with multipath, fading, and noise.  

Furthermore, the dataset provides other features such as Fast Fourier Transform (FFT), Autocorrelation (ACF), and Power spectral Density (PSD) in linear scale.

The dataset can be used to validate the designed classifier model aiming to identify cellular communication signals.

For each signal, the dimension of SCF estimate (alpha domain profile maximizing over spectral frequency) is 1*32769. There are four train sets which must be used together (SCF_train1.mat, SCF_train2.mat, SCF_train3.mat, and SCF_train4.mat). Four train sets have 3000 signals totally, and the test set has 1500. 

The label of the cellular communication standards are given in dataset as follows:

WCDMA -> 0
LTE -> 1
GSM -> 2

The compressed file includes:

1. ACF Folder
2. FFT Folder
3. PSD Folder
4. SCF Folder

Each folder above consists of two folder: Test and Train. The test set is located in the Test folder as two parts and the train set is located in the Train folder as four parts.

The contents of .mat files:

training_class_k : denotes class labels corresponding to the training_data_k, its dimension is 750*1 double
training_data_k : includes the kth quarter of the training data, its dimension is 750*32769 double

test_class_k : denotes class labels corresponding to the test_data_k, its dimension is 750*1 double
test_data_k : includes the kth  half of the test data, its dimension is 750*32769 double


The dataset has been used for the paper "Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines" submitted for possible publication in IEEE Access. Please cite this paper, if you use the dataset.
